Combined analysis of different logs in quantification of exhumation and its implications for hydrocarbon exploration, a case study from Australia

Exhumation in the Eromanga Basin of South Australia and Queensland has been quantified using the compaction methodology. The standard method of estimating exhumation using the sonic log has been modified and the adjusted sonic, the bulk density and neutron logs, have been used to estimate exhumation. Additionally the use of a single shale has not been adopted, and seven units, ranging in age from Cretaceous to Jurassic have been analysed. All units yield similar results; and burial at depth greater than currently observed is the most likely cause of overcompaction. The use of the adjusted sonic, bulk and neutron logs have been justified. This study has major implications for hydrocarbon exploration since predicted maturation of source rocks will be greater for any given geothermal history if exhumation is incorporated in maturation modelling

Diagnostic tools of energy performance for supermarkets using Artificial Neural Network algorithms

Supermarket performance monitoring is of vital importance to ensure systems perform adequately and guarantee operating costs and energy use are kept at a minimum. Furthermore, advanced monitoring techniques can allow early detection of equipment faults that could disrupt store operation. This paper details the development of a tool for performance monitoring and fault detection for supermarkets focusing on evaluating the Store's Total Electricity Consumption as well as individual systems, such as Refrigeration, HVAC, Lighting and Boiler. Artificial Neural Network (ANN) models are developed for each system to provide the energy baseline, which is modelled as a dependency between the energy consumption and suitable explanatory variables. The tool has two diagnostic levels. The first level broadly evaluates the systems performance, in terms of energy consumption, while the second level applies more rigorous criteria for fault detection of supermarket subsystems. A case study, using data from a store in Southeast England, is presented and results show remarkable accuracy for calculating hourly energy use, thus marking the ANN method as a viable tool for diagnosis purposes. Finally, the generic nature of the methodology approach allows the development and application to other stores, effectively offering a valuable analytical tool for better running of supermarkets

Multi-stage optimal design of energy systems for urban districts

Urban districts develop in a dynamic manner over multi-year horizons with new buildings being added and changes being made to existing buildings (e.g. retrofits). Nevertheless, optimization models used to design urban district energy systems (DES) commonly consider a single, “typical” year for the design. This practice, however, does not allow for energy design decisions to be made in multiple phases in order to reflect a district’s development phases. This paper addresses this issue and presents a novel optimization model that allows the multi-stage optimal design of urban DES. The model identifies the cost-optimal technology investment decisions across a horizon that spans multiple years, while also calculating the energy system’s optimal operating patterns in order to meet the district’s energy demands. The evolution of the district’s energy demands and aspects like the evolution of technology costs and energy carrier prices are considered in the model. The model is applied to a new urban district in Zurich, Switzerland, for which 5 development stages are considered with new buildings of various types constructed in each phase. A multi-stage DES design plan is developed for the period 2021-2050, which includes large energy technology investments for each new development phase, but also smaller ones in the intermediate years between 2021 and 2050. The model specifies the amount of energy generated by each technology installed in each year, as well as the contribution of renewable energy in covering the district’s energy demands

A GIS based methodology to support multi-criteria decision making for the retrofitting process of residential buildings

This paper presents a workflow to support the decision making for building retrofit and building systems update at urban scale. The workflow includes i) a method to extract information from a geographical information system including information on building characteristics, building systems and building typology, ii) a method to evaluate the current and future energy demand of buildings using a dynamic building simulation tool, and iii) an updated version of the energy hub approach to evaluate best performing options in terms of energy systems update. The developed method is applied to the city of Zurich to evaluate the optimal energy system update for all existing buildings within the city. Modelling results include best performing options in terms of CO2 emissions, renewable energy share, or energy efficiency while minimizing resulting costs for possible system and retrofitting solutions

Seasonal predictability of the 2010 Russian heat wave

The atmospheric blocking over eastern Europe and western Russia that prevailed during July and August of 2010 led to the development of a devastating Russian heat wave. Therefore the question of whether the event was predictable or not is highly important. The principal aim of this study is to examine the predictability of this high-impact atmospheric event on a seasonal timescale. To this end, a set of dynamical seasonal simulations have been carried out using an atmospheric global circulation model (AGCM). The impact of various model initializations on the predictability of this large-scale event and its sensitivity to the initial conditions has been also investigated. The ensemble seasonal simulations are based on a modified version of the lagged-average forecast method using different lead-time initializations of the model. The results indicated that only a few individual members reproduced the main features of the blocking system 3 months ahead. Most members missed the phase space and the propagation of the system, setting limitations in the predictability of the event

Identification of protein kinase inhibitors with a selective negative effect on the viability of Epstein-Barr virus infected B cell lines

Epstein-Barr virus (EBV) is a human herpesvirus, which is causally associated with the development of several B lymphocytic malignancies that include Burkitt's lymphomas, Hodgkin's disease, AIDS and posttransplant associated lymphomas. The transforming activity of EBV is orchestrated by several latent viral proteins that mimic and modulate cellular growth promoting and antiapoptotic signaling pathways, which involve among others the activity of protein kinases. In an effort to identify small molecule inhibitors of the growth of EBV-transformed B lymphocytes a library of 254 kinase inhibitors was screened. This effort identified two tyrosine kinase inhibitors and two MEK inhibitors that compromised preferentially the viability of EBV-infected human B lymphocytes. Our findings highlight the possible dependence of EBV-infected B lymphocytes on specific kinase-regulated pathways underlining the potential for the development of small molecule-based therapeutics that could target selectively EBV-associated human B lymphocyte malignancies. © 2014 Mavromatidis et al

Visualizing big network traffic data using frequent pattern mining and hypergraphs

Visualizing communication logs, like NetFlow records, is extremely useful for numerous tasks that need to analyze network traffic traces, like network planning, performance monitoring, and troubleshooting. Communication logs, however, can be massive, which necessitates designing effective visualization techniques for large data sets. To address this problem, we introduce a novel network traffic visualization scheme based on the key ideas of (1) exploiting frequent itemset mining (FIM) to visualize a succinct set of interesting traffic patterns extracted from large traces of communication logs; and (2) visualizing extracted patterns as hypergraphs that clearly display multi-attribute associations. We demonstrate case studies that support the utility of our visualization scheme and show that it enables the visualization of substantially larger data sets than existing network traffic visualization schemes based on parallel-coordinate plots or graphs. For example, we show that our scheme can easily visualize the patterns of more than 41 million NetFlow records. Previous research has explored using parallel-coordinate plots for visualizing network traffic flows. However, such plots do not scale to data sets with thousands of even millions of flows

Photovoltaic panels as a main component of energy sustainable communities : comparative energy analysis of a village under Swiss and South African climatic loads

Paper presented to the 3rd Southern African Solar Energy Conference, South Africa, 11-13 May, 2015.At the community level, it is difficult to rely on a single centralized energy technology when considering renewable energy and the use of a mix of multiple distributed energy systems (DES) seems advantageous. DES, e.g. photovoltaic panels (PV), are typically integrated at building level and account for a small fraction of required energy. Since energy supply from renewables is highly fluctuating over time and dependent on climatic and local conditions, a reliable integration is a challenging task. In this paper, we use a recently developed concept, that allows to sufficiently improve the energy efficiency of the building stock, to manage energy supply from renewables and to optimize the future energy system using the energy hub approach, while effectively integrating DES. Using the same village characteristics, we found that, due to mismatch of available solar potential and the electricity demand, 18% of available solar potential cannot be utilized in Zernez, while in Johannesburg, this mismatch amounts to 22%.cf201